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Identification and Inference in a Simultaneous Equation Under Alternative Information Sets and Sampling Schemes

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  • Jan F. KIVIET

    (Division of Economics, Nanyang Technological University, Singapore 637332, Singapore)

Abstract

In simple static linear simultaneous equation models the empirical distributions of IV and OLS are examined under alternative sampling schemes and compared with their first-order asymptotic approximations. We demonstrate that the limiting distribution of consistent IV is not affected by conditioning on exogenous regressors, whereas that of inconsistent OLS is. The OLS asymptotic and simulated actual variances are shown to diminish by extending the set of exogenous variables kept fixed in sampling, whereas such an extension disrupts the distribution of IV and deteriorates the accuracy of its standard asymptotic approximation, not only when instruments are weak. Against this background the consequences for the identification of parameters of interest are examined for a set- ting in which (in practice often incredible) assumptions regarding the zero correlation between instruments and disturbances are replaced by (generally more credible) inter- val assumptions on the correlation between endogenous regressor and disturbance. This yields OLS-based modified confidence intervals, which are usually conservative. Often they compare favorably with IV-based intervals and accentuate their frailty.

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Bibliographic Info

Paper provided by Nanyang Technolgical University, School of Humanities and Social Sciences, Economic Growth centre in its series Economic Growth centre Working Paper Series with number 1207.

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Length: 38 pages
Date of creation: Jul 2012
Date of revision:
Handle: RePEc:nan:wpaper:1207

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Keywords: partial identification; weak instruments; (un)restrained repeated sampling; (un)conditional (limiting) distributions; credible robust inference;

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Citations

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Cited by:
  1. Doko Tchatoka, Firmin & Dufour, Jean-Marie, 2012. "Identification-robust inference for endogeneity parameters in linear structural models," MPRA Paper 40695, University Library of Munich, Germany.
  2. Skeels, Christopher L. & Taylor, Larry W., 2014. "Prediction after IV estimation," Economics Letters, Elsevier, vol. 122(3), pages 420-422.
  3. Bun, Maurice J.G. & Harrison, Teresa D., 2014. "OLS and IV estimation of regression models including endogenous interaction terms," School of Economics Working Paper Series 2014-3, LeBow College of Business, Drexel University.
  4. Denizer, Cevdet & Kaufmann, Daniel & Kraay, Aart, 2013. "Good countries or good projects? Macro and micro correlates of World Bank project performance," Journal of Development Economics, Elsevier, vol. 105(C), pages 288-302.

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